App feedback model for NLP tasks
Description
The objective is to utilize Natural Language Processing (NLP) with comment reviews for Appfeedback, specifically to identify named entities (NER), profanity, and stop words, and provide an automated approach to pre-processing and cleaning text for downstream analytics tools. The system uses spaCy's en_core_web_sm model, an open-source software and model, to analyze text reviews from various sources, including external sources (Google and Apple stores) and internal sources (FeedbackUI and VA Mobile). The model provides an out-of-the-box approach for NLP tasks, including NER, profanity detection, and stop word removal. The data sources include: Text reviews from the VA's mobile applications on the Google and Apple stores (external sources) Reviews from FeedbackUI (internal source, available in the OIA_MobileHealth database) Reviews from VA Mobile (internal source, available via CSV files on the Mobile VA's internal website) Users: The users of this system are likely the OCC Data Science Team, who are responsible for developing and maintaining the pipeline. Target Audience: The target audience mobile application developers and other internal stakeholders who may be interested in analyzing and understanding the sentiment and feedback from these reviews. Problem to be solved: Consolidating feedback provided by patients and providers for VA Mobile Apps. Simple aggregation of data that provides a dashboard for clients to view and monitor trends.
Detailed example
Identified issues shown within Power BI dashboard.
AI / analytics pattern
Natural Language Processing: AI that processes, interprets, and shares information in human language.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
Decrease working hours in manual review of feedback.
Controls / human review
ATO: Not reported; PIA: Not published